Lattice QCD calculation of ππ\pi\pi scattering length

We study s-wave pion-pion ($\pi\pi$) scattering length in lattice QCD for pion masses ranging from 330 MeV to 466 MeV. In the "Asqtad" improved staggered fermion formulation, we calculate the $\pi\pi$ four-point functions for isospin I=0 and 2 channels, and use chiral perturbation theory at next-to-leading order to extrapolate our simulation results. Extrapolating to the physical pion mass gives the scattering lengths as $m_\pi a_0^{I=2} = -0.0416(2)$ and $m_\pi a_0^{I=0} = 0.186(2)$ for isospin I=2 and 0 channels, respectively. Our lattice simulation for $\pi\pi$ scattering length in the I=0 channel is an exploratory study, where we include the disconnected contribution, and our preliminary result is near to its experimental value. These simulations are performed with MILC 2+1 flavor gauge configurations at lattice spacing $a \approx 0.15$ fm.Comment: Remove some typo

Methyl isonicotinate 1-oxide

In the title compound, C7H7NO3, the benzene ring and the methyl ester group are nearly coplanar, forming a dihedral of 3.09 (9)°. The crystal structure is stabilized by inter­molecular C—H⋯O hydrogen bonds, forming layers parallel to (101)

Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted strategies of sliding window over the entire video or exhaustively ranking all possible clip-sentence pairs in a pre-segmented video, which inevitably suffer from exhaustively enumerated candidates. To alleviate this problem, we formulate this task as a problem of sequential decision making by learning an agent which regulates the temporal grounding boundaries progressively based on its policy. Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training. Our proposed framework achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset and Charades-STA dataset while observing only 10 or less clips per video.Comment: AAAI 201